{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import scipy.io as sio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['__globals__', '__version__', '__header__', 'X'])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat = sio.loadmat('./data/ex7data1.mat')\n",
    "mat.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.381563</td>\n",
       "      <td>3.389113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.527875</td>\n",
       "      <td>5.854178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.655682</td>\n",
       "      <td>4.411995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.765235</td>\n",
       "      <td>3.715414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.846560</td>\n",
       "      <td>4.175506</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         X1        X2\n",
       "0  3.381563  3.389113\n",
       "1  4.527875  5.854178\n",
       "2  2.655682  4.411995\n",
       "3  2.765235  3.715414\n",
       "4  2.846560  4.175506"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = pd.DataFrame(mat.get('X'), columns=['X1', 'X2'])\n",
    "data1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set(context=\"notebook\", style=\"white\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11279a550>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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88YSmT5+u1atXKycnp16vB/epGkUNTr9Yfbu10+D0i2noAhBxfMFgMPhrv2nBggV6++23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\nRRfKz2P6jOAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11279a9e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('X1', 'X2', data=data1, fit_reg=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# data2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X1</th>\n",
       "      <th>X2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.842080</td>\n",
       "      <td>4.607572</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.658583</td>\n",
       "      <td>4.799964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.352579</td>\n",
       "      <td>3.290854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.904017</td>\n",
       "      <td>4.612204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.231979</td>\n",
       "      <td>4.939894</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         X1        X2\n",
       "0  1.842080  4.607572\n",
       "1  5.658583  4.799964\n",
       "2  6.352579  3.290854\n",
       "3  2.904017  4.612204\n",
       "4  3.231979  4.939894"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat = sio.loadmat('./data/ex7data2.mat')\n",
    "data2 = pd.DataFrame(mat.get('X'), columns=['X1', 'X2'])\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x11294b8d0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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g35Faeg6GY1BVDapTAzyAQ5ZQP9eDc5fVw+d1cetTGWAgE1HJWL0XNqrGsX1/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11499a9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot('X1', 'X2', data=data2, fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
